{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Document loading, retrieval methods and text splitting\n",
    "# !pip install -qU langchain langchain_community\n",
    "\n",
    "# # Local vector store via Chroma\n",
    "# !pip install -qU langchain_chroma\n",
    "\n",
    "# # Local inference and embeddings via Ollama\n",
    "# !pip install -qU langchain_ollama\n",
    "\n",
    "# # Web Loader\n",
    "# !pip install -qU beautifulsoup4\n",
    "\n",
    "# # Pull the model first\n",
    "# !ollama pull nomic-embed-text\n",
    "\n",
    "# !pip install -qU pypdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imports\n",
    "import os\n",
    "import glob\n",
    "from dotenv import load_dotenv\n",
    "import gradio as gr\n",
    "from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader\n",
    "from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter\n",
    "from langchain_chroma import Chroma\n",
    "from langchain_ollama import OllamaEmbeddings\n",
    "from langchain_ollama import ChatOllama\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.runnables import RunnablePassthrough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in documents using LangChain's loaders\n",
    "# Take everything in all the sub-folders of our knowledgebase\n",
    "\n",
    "folders = glob.glob(\"Manuals/*\")\n",
    "\n",
    "def add_metadata(doc, doc_type):\n",
    "    doc.metadata[\"doc_type\"] = doc_type\n",
    "    return doc\n",
    "\n",
    "documents = []\n",
    "for folder in folders:\n",
    "    doc_type = os.path.basename(folder)\n",
    "    loader = DirectoryLoader(folder, glob=\"**/*.pdf\", loader_cls=PyPDFLoader)\n",
    "    folder_docs = loader.load()\n",
    "    documents.extend([add_metadata(doc, doc_type) for doc in folder_docs])\n",
    "\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
    "chunks = text_splitter.split_documents(documents)\n",
    "\n",
    "print(f\"Total number of chunks: {len(chunks)}\")\n",
    "print(f\"Document types found: {set(doc.metadata['doc_type'] for doc in documents)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n",
    "# Chroma is a popular open source Vector Database based on SQLLite\n",
    "DB_NAME = \"vector_db\"\n",
    "\n",
    "embeddings = OllamaEmbeddings(model=\"nomic-embed-text\")\n",
    "\n",
    "# Delete if already exists\n",
    "\n",
    "if os.path.exists(DB_NAME):\n",
    "    Chroma(persist_directory=DB_NAME, embedding_function=embeddings).delete_collection()\n",
    "\n",
    "# Create vectorstore\n",
    "\n",
    "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=DB_NAME)\n",
    "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#run a quick test - should return a list of documents = 4\n",
    "question = \"What kind of grill is the Spirt II?\"\n",
    "docs = vectorstore.similarity_search(question)\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new Chat with Ollama\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain.chains import ConversationalRetrievalChain\n",
    "MODEL = \"llama3.2:latest\"\n",
    "llm = ChatOllama(temperature=0.7, model=MODEL)\n",
    "\n",
    "# set up the conversation memory for the chat\n",
    "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
    "\n",
    "# the retriever is an abstraction over the VectorStore that will be used during RAG\n",
    "retriever = vectorstore.as_retriever()\n",
    "\n",
    "# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n",
    "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try a simple question\n",
    "\n",
    "query = \"How do I change the water bottle ?\"\n",
    "result = conversation_chain.invoke({\"question\": query})\n",
    "print(result[\"answer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up a new conversation memory for the chat\n",
    "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
    "\n",
    "# putting it together: set up the conversation chain with the  LLM, the vector store and memory\n",
    "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wrapping that in a function\n",
    "\n",
    "def chat(question, history):\n",
    "    result = conversation_chain.invoke({\"question\": question})\n",
    "    return result[\"answer\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we will bring this up in Gradio using the Chat interface -\n",
    "\n",
    "A quick and easy way to prototype a chat with an LLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And in Gradio:\n",
    "\n",
    "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)"
   ]
  }
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